Most engineering teams treat "AI-assisted docs" as a single, monolithic decision applied to an entire file. You either let the model write the whole README or you don't. Then you argue about quality after the fact. That framing fails for a mundane reason: it ignores that different sections of a document have radically different risks. A new post on DEV.to argues that the fastest way to make generated documentation trustworthy is to decide, per section, whether a model is allowed to write that section at all.

The Problem With Binary Decisions

When you apply a blanket "AI can write docs" policy, you are essentially asking a language model to hallucinate architecture. The model doesn't know if the API endpoint described in section 3 actually returns a 404 or a 200. It doesn't know if the setup instructions in section 5 still work after last week's dependency update. By treating the file as a single unit of ownership, you dilute accountability. You end up with a document that is partially correct, partially plausible, and entirely unverifiable without a human reading every single line.

Triage by Testability

The proposed solution is "doc ownership triage." Instead of asking "Can AI write this?", you ask "Can we test this?" If a section describes a function signature, a CLI flag, or an environment variable, it is testable. You can write a script that checks if --verbose exists in the help output. If a section describes a high-level architectural pattern or a user journey, it is not easily testable. You can't unit test a philosophy. This distinction should dictate who holds the pen. AI should be allowed to generate the testable parts because we can verify them automatically. Humans must own the untestable parts because they require context and judgment.

Practical Implementation for Builders

For teams using tools like GitHub Copilot or other LLM-based documentation assistants, this means changing your workflow. Stop prompting for "write the documentation for this class." Start prompting for "write the API reference for this class, ensuring every parameter is cross-checked against the codebase." The output should be treated as a draft for a specific, verifiable section. If the section is "Installation," the AI can generate the commands, but a CI job must run those commands in a fresh container to prove they work. If the section is "Design Philosophy," the AI should be muted. You cannot automate the "why," only the "how."

Key Takeaways

  • Treat documentation files as collections of distinct sections with different ownership rules, not as single units.
  • Allow AI to generate content only for sections that can be verified through automated tests or CI checks.
  • Require human ownership for high-level architectural descriptions, user journeys, and design philosophies.
  • Use CI/CD pipelines to validate AI-generated technical snippets (like CLI flags) before merging to main.

The Bottom Line

If you can't test it, you can't trust the robot to write it. Stop letting LLMs hallucinate your architecture and start using them to draft your API references.